arXiv cs.AI· Sudeshna Jana, Siddhartha Laha, Manjira Sinha, Tirthankar Dasgupta, Pabitra Mitra·· 3 小时前AI 评分26
超越躯体疾病：临床叙事能否揭示慢病住院患者的新发心理健康风险？
Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
AI 导读
一项新研究探索能否从纵向临床叙事文本中推断慢性病住院患者的潜在心理健康风险。团队基于 MIMIC-III 用出院小结构建了约 4,000 名患者的弱监督队列，提出分层状态空间框架，从逐日临床叙事提取心理健康表征并建模其时间演化。初步结果显示二元风险预测性能提升，且风险与无风险队列的模型推导风险轨迹明显不同。
正文 · 原文
Abstract:Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention. However, most existing mental health studies focus on patients with established psychiatric conditions rather than patients hospitalized primarily for physical illness. In this work, we investigate whether latent mental-health risk in chronically ill patients can be inferred from longitudinal clinical narratives. We construct a weakly supervised cohort around 4,000 patients from MIMIC-III using mental-health evidence extracted from discharge summaries. A hierarchical state-space framework derives mental-health representations from day-wise clinical narratives and models their temporal evolution for risk estimation throughout hospitalization. Preliminary results demonstrate improved binary risk prediction using the proposed representations and show distinct model derived risk trajectories between risk and no-risk cohorts, providing a foundation for studying emerging mental-health risks in chronically ill patients.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.35761 [cs.AI] |
| (or arXiv:2610.35761v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35761 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sudeshna Jana [view email]
[v1]
Mon, 26 Oct 2026 17:06:33 UTC (144 KB)
来源：arXiv cs.AI · arxiv.org